57% and 100% on the selected dataset, which can well complete the vehicle lane-change detection task.In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history. To boost the performance of offspring generation in the real-coded genetic algorithm (RCGA), in this paper, we propose to exploit the search history cached so far in an online style during the iteration. Specifically, survivor individuals over the past few generations are collected and stored in the archive to form the search history. We introduce a simple yet effective crossover model driven by the search history (abbreviated as SHX). In particular, the search history is clustered, and each cluster is assigned a score for SHX. In essence, the proposed SHX is a data-driven method which exploits the search history to perform offspring selection after the offspring generation. Since no additional fitness evaluations are needed, SHX is favorable for the tasks with limited budget or expensive fitness evaluations. We experimentally verify the effectiveness of SHX over 15 benchmark functions. Quantitative results show that our SHX can significantly enhance the performance of RCGA, in terms of both accuracy and convergence speed. Also, the induced additional runtime is negligible compared to the total processing time.Air pollutant concentration forecasting is an effective way which protects health of the public by the warning of the harmful air contaminants. In this study, a hybrid prediction model has been established by using information gain, wavelet decomposition transform technique, and LSTM neural network, and applied to the daily concentration prediction of atmospheric pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) in Beijing. First, the collected raw data are selected by feature selection by information gain, and a set of factors having a strong correlation with the prediction is obtained. Then, the historical time series of the daily air pollutant concentration is decomposed into different frequencies by using a wavelet decomposition transform and recombined into a high-dimensional training data set. Finally, the LSTM prediction model is trained with high-dimensional data sets, and the parameters are adjusted by repeated tests to obtain the optimal prediction model. The data used in this study were derived from six air pollution concentration data in Beijing from 1/1/2014 to 31/12/2016, and the atmospheric pollutant concentration data of Beijing between 1/1/2017 and 31/12/2017 were used to test the predictive ability of the data set test model. The results show that the evaluation index MAPE of the model prediction is 7.45%. Therefore, the hybrid prediction model has a higher value of application for atmospheric pollutant concentration prediction, because this model has higher prediction accuracy and stability for future air pollutant concentration prediction.Three hexacoordinated octahedral nickel (II) complexes, [Ni (Trp-sal) (phen) (CH3OH)] (1), [Ni (Trp-o-van) (phen) (CH3OH)]?2CH3OH (2), and [Ni (Trp-naph) (phen) (CH3OH)] (3) (where Trp-sal?=?Schiff base derived from tryptophan and salicylaldehyde, Trp-o-van?=?Schiff base derived from tryptophan and o-vanillin, Trp-naph?=?Schiff base derived from tryptophan and 2-hydroxy-1-naphthaldehyde, phen?=?1, 10-phenanthroline), have been synthesized and characterized as potential anticancer agents. Details of structural study of these complexes using single-crystal X-ray crystallography showed that distorted octahedral environment around nickel (II) ion has been satisfied by three nitrogen atoms and three oxygen atoms. All these complexes displayed moderate cytotoxicity toward esophageal cancer cell line Eca-109 with the IC50 values of 23.95?±?2.54?μM for 1, 18.14?±?2.39?μM for 2, and 21.89?±?3.19?μM for 3. Antitumor mechanism studies showed that complex 2 can increase the autophagy, reactive oxygen species (ROS) levels, and decrease the mitochondrial membrane potential remarkably in a dose-dependent manner in the Eca-109 cells. Complex 2 can cause cell cycle arrest in the G2/M phase. Additionally, complex 2 can regulate the Bcl-2 family and autophagy-related proteins.The ongoing pandemic of coronavirus disease 2019 (COVID-19) has led to global health and healthcare crisis, apart from the tremendous socioeconomic effects. One of the significant challenges in this crisis is to identify and monitor the COVID-19 patients quickly and efficiently to facilitate timely decisions for their treatment, monitoring, and management. Research efforts are on to develop less time-consuming methods to replace or to supplement RT-PCR-based methods. The present study is aimed at creating efficient deep learning models, trained with chest X-ray images, for rapid screening of COVID-19 patients. We used publicly available PA chest X-ray images of adult COVID-19 patients for the development of Artificial Intelligence (AI)-based classification models for COVID-19 and other major infectious diseases. https://www.selleckchem.com/products/Amprenavir-(Agenerase).html To increase the dataset size and develop generalized models, we performed 25 different types of augmentations on the original images. Furthermore, we utilized the transfer learning approach for the training and testing of the classification models. The combination of two best-performing models (each trained on 286 images, rotated through 120° or 140° angle) displayed the highest prediction accuracy for normal, COVID-19, non-COVID-19, pneumonia, and tuberculosis images. AI-based classification models trained through the transfer learning approach can efficiently classify the chest X-ray images representing studied diseases. Our method is more efficient than previously published methods. It is one step ahead towards the implementation of AI-based methods for classification problems in biomedical imaging related to COVID-19.Despite the disrepute spiders have had for centuries, their bite is a rare occurrence. In the Mediterranean area, only two of the numerous known species are considered of medical significance Latrodectus tredecimguttatus and Loxosceles rufescens. Spider bites have no pathognomonic signs or symptoms, therefore most diagnoses are presumptive; a spider bite can only be diagnosed when a spider (seen at the time of the bite) is collected and identified by an expert, since most physicians and patients are unable to recognize a certain spider species or distinguish spiders from other arthropods. Skin lesions of uncertain etiology are too often attributed to spider bites. In most cases, these are actually skin and soft-tissue infections, allergic reactions, dermatoses etc. Misdiagnosing a wound as a spider bite can lead to delays in appropriate care, cause adverse or even fatal outcomes and have medical-legal implications. Concerningly, misinformation on spider bites also affects the medical literature and it appears there is lack of awareness on current therapeutic indications for verified bites.